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	Analýza dynamickej modularity	Analýza multiplexných sietí
Odbor	Analýza sietí	Analýza sietí
Rodina	Machine learning	Machine learning
Rok vzniku	2010	2014
Tvorca	Mucha, P. J.; Porter, M. A.; and colleagues	Kivela, M.; Boccaletti, S. et al.
Typ	Community detection on temporal networks	Structural network model
Pôvodný zdroj	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Ďalšie názvy	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Príbuzné	5	6
Zhrnutie	Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.